To enhance the generalizability of the embeddings, we also leverage the unlabeled data by the auxiliary contrastive self-supervised learning [1]}. At each iteration, we sample \(N\)  images either from training or testing set,Note that it is valid to access the images from testing set in few-shot learning, which is named transductive few-shot learning [2]}. and generate 2 augmentations for each images, i.e., each augmented image has 1 positive example and \(2N-1\)  corresponding negative examples with the self-supervised loss defined as follows.
\(\small L_{self} = \frac{1}{2N} \sum _{k=1}^{N}[\ell (2k-1, 2k)+\ell (2k, 2k-1)].\) 
